42,102 research outputs found
Tabling as a Library with Delimited Control
Tabling is probably the most widely studied extension of Prolog. But despite
its importance and practicality, tabling is not implemented by most Prolog
systems. Existing approaches require substantial changes to the Prolog engine,
which is an investment out of reach of most systems. To enable more widespread
adoption, we present a new implementation of tabling in under 600 lines of
Prolog code. Our lightweight approach relies on delimited control and provides
reasonable performance.Comment: 15 pages. To appear in Theory and Practice of Logic Programming
(TPLP), Proceedings of ICLP 201
XMDS2: Fast, scalable simulation of coupled stochastic partial differential equations
XMDS2 is a cross-platform, GPL-licensed, open source package for numerically
integrating initial value problems that range from a single ordinary
differential equation up to systems of coupled stochastic partial differential
equations. The equations are described in a high-level XML-based script, and
the package generates low-level optionally parallelised C++ code for the
efficient solution of those equations. It combines the advantages of high-level
simulations, namely fast and low-error development, with the speed, portability
and scalability of hand-written code. XMDS2 is a complete redesign of the XMDS
package, and features support for a much wider problem space while also
producing faster code.Comment: 9 pages, 5 figure
Learning long-range spatial dependencies with horizontal gated-recurrent units
Progress in deep learning has spawned great successes in many engineering
applications. As a prime example, convolutional neural networks, a type of
feedforward neural networks, are now approaching -- and sometimes even
surpassing -- human accuracy on a variety of visual recognition tasks. Here,
however, we show that these neural networks and their recent extensions
struggle in recognition tasks where co-dependent visual features must be
detected over long spatial ranges. We introduce the horizontal gated-recurrent
unit (hGRU) to learn intrinsic horizontal connections -- both within and across
feature columns. We demonstrate that a single hGRU layer matches or outperforms
all tested feedforward hierarchical baselines including state-of-the-art
architectures which have orders of magnitude more free parameters. We further
discuss the biological plausibility of the hGRU in comparison to anatomical
data from the visual cortex as well as human behavioral data on a classic
contour detection task.Comment: Published at NeurIPS 2018
https://papers.nips.cc/paper/7300-learning-long-range-spatial-dependencies-with-horizontal-gated-recurrent-unit
Query Rewriting and Optimization for Ontological Databases
Ontological queries are evaluated against a knowledge base consisting of an
extensional database and an ontology (i.e., a set of logical assertions and
constraints which derive new intensional knowledge from the extensional
database), rather than directly on the extensional database. The evaluation and
optimization of such queries is an intriguing new problem for database
research. In this paper, we discuss two important aspects of this problem:
query rewriting and query optimization. Query rewriting consists of the
compilation of an ontological query into an equivalent first-order query
against the underlying extensional database. We present a novel query rewriting
algorithm for rather general types of ontological constraints which is
well-suited for practical implementations. In particular, we show how a
conjunctive query against a knowledge base, expressed using linear and sticky
existential rules, that is, members of the recently introduced Datalog+/-
family of ontology languages, can be compiled into a union of conjunctive
queries (UCQ) against the underlying database. Ontological query optimization,
in this context, attempts to improve this rewriting process so to produce
possibly small and cost-effective UCQ rewritings for an input query.Comment: arXiv admin note: text overlap with arXiv:1312.5914 by other author
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